Abstract
Single-cell optimization objective and trade-off inference (SCOOTI) is a computational framework that integrates bulk and single-cell omics data with genome-scale metabolic modeling to infer metabolic objectives and trade-offs in biological systems. Here, we present a protocol for installing and running SCOOTI, using transcriptomics, proteomics, and metabolomics data to constrain metabolic models. We describe steps to interpret metabolic priorities across different cell states or conditions through clustering, dimensionality reduction, and trade-off analysis.